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首次观测 · 记录 #1CVF · CVPR 2026:cvf:bf31fe593eb47398661286d9cd73583dCVF · CVPR 2026

TAlignDiff: Automatic Tooth Alignment assisted by Diffusion-based Transformation Learning

Liu, Yunbi, Tang, Enqi, Li, Shiyu, Shuai, Hui, Ma, Lei, Li, Juncheng, Yu, Kuai, Lou, Shu 等 10 位作者

原文摘要

Orthodontic treatment hinges on tooth alignment, which significantly affects occlusal function, facial aesthetics, and patients' quality of life. Current deep learning approaches often predict transformation matrices for the misaligned tooth point cloud via point-to-point geometric constraints to achieve tooth alignment, ignoring the clinically specific distributions exhibited in these matrices. To address this, we introduce a new automatic tooth alignment method named TAlignDiff, which is assisted by diffusion-based transformation learning. TAlignDiff comprises two main components: a primary point cloud-based regression network (PRN) and a diffusion-based transformation matrix denoising module (DTMD). Geometry-constrained losses supervise PRN learning for point cloud-level alignment. DTMD, as an auxiliary module, learns the latent distribution of matrices from clinical data. We integrate point cloud-based transformation regression and diffusion-based transformation modeling into a unified framework, allowing bidirectional feedback between geometric constraints and diffusion refinement. We validate our method on a challenge dataset from clinical practice and an extra orthodontic dataset. Its efficacy was confirmed through effective ablation studies and comparative analyses, highlighting its potential for application in orthodontic treatment.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:bf3c51e523e016d69f0c4eb3550d0a27CVF · CVPR 2026

Diffusion-Based sRGB Real Noise Generation via Prompt-Driven Noise Representation Learning

Ko, Jaekyun, Kim, Dongjin, Lee, Soomin, Wang, Guanghui, Kim, Tae Hyun

原文摘要

Denoising in the sRGB image space is challenging due to large noise variability. Although end-to-end methods perform well, their effectiveness in real-world scenarios is limited by the scarcity of real noisy-clean image pairs, which are expensive and difficult to collect. To address this limitation, several generative methods have been developed to synthesize realistic noisy images from limited data. These approaches often rely on camera metadata during both training and testing to synthesize real-world noise. However, the lack of metadata or inconsistencies between devices restricts their usability. Therefore, we propose a novel framework called Prompt-Driven Noise Generation (PNG). This model is capable of acquiring high-dimensional prompt features that capture the characteristics of real-world input noise and creating a variety of realistic noisy images consistent with the distribution of the input noise. By eliminating the dependency on explicit camera metadata, our approach significantly enhances the generalizability and applicability of noise synthesis. Comprehensive experiments reveal that our model effectively produces realistic noisy images and show the successful application of these generated images in removing real-world noise across various benchmark datasets.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:bf4a15f515098505258ba39037ffb993CVF · CVPR 2026

Uncertainty-guided Compositional Alignment with Part-to-Whole Semantic Representativeness in Hyperbolic Vision-Language Models

Kim, Hayeon, Jang, Ji Ha, Kim, Junghun James, Chun, Se Young

原文摘要

While Vision-Language Models (VLMs) have achieved remarkable performance, their Euclidean embeddings remain limited in capturing hierarchical relationships such as part-to-whole or parent-child structures, and often face challenges in multi-object compositional scenarios. Hyperbolic VLMs mitigate this issue by better preserving hierarchical structures and modeling part-whole relations (i.e., whole scene and its part images) through entailment. However, existing approaches do not model that each part has a different level of semantic representativeness to the whole. We propose UNcertainty-guided Compositional Hyperbolic Alignment (UNCHA) for enhancing hyperbolic VLMs. UNCHA models part-to-whole semantic representativeness with hyperbolic uncertainty, by assigning lower uncertainty to more representative parts and higher uncertainty to less representative ones for the whole scene. This representativeness is then incorporated into the contrastive objective with uncertainty-guided weights. Finally, the uncertainty is further calibrated with an entailment loss regularized with entropy-based term. With the proposed losses, UNCHA learns hyperbolic embeddings with more accurate part-whole ordering, capturing the underlying compositional structure in an image and improving its understanding of complex multi-object scenes. UNCHA achieves state-of-the-art performance on zero-shot classification, retrieval, and multi-label classification benchmarks. Our code and models are available at: https://github.com/jeeit17/UNCHA.git.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:bf593aef9458b5eff28afcac397a18ceCVF · CVPR 2026

Efficient and High-Fidelity Omni Modality Retrieval

Huynh, Chuong, Luong, Manh, Shrivastava, Abhinav

原文摘要

Multimodal retrieval is the task of aggregating information from queries across heterogeneous modalities to retrieve desired targets. State-of-the-art multimodal retrieval models can understand complex queries, yet they are typically limited to two modalities: text and vision. This limitation impedes the development of universal retrieval systems capable of comprehending queries that combine more than two modalities. To advance toward this goal, we present OmniRet, the first retrieval model capable of handling complex, composed queries spanning three key modalities: text, vision, and audio. Our OmniRet model addresses two critical challenges for universal retrieval: computational efficiency and representation fidelity. First, feeding massive token sequences from modality-specific encoders to Large Language Models (LLMs) is computationally inefficient. We therefore introduce an attention-based resampling mechanism to generate compact, fixed-size representations from these sequences. This shared module is designed to maintain representational diversity and generalization capabilities while remaining sensitive to modality-specific information. Second, compressing rich omni-modal data into a single embedding vector inevitably causes information loss and discards fine-grained details. We propose Attention Sliced Wasserstein Pooling to preserve these fine-grained details, leading to improved omni-modal representations. OmniRet is trained on an aggregation of approximately 6 million query-target pairs spanning 30 datasets. We benchmark our model on 13 retrieval tasks and a MMEBv2 subset. Our model demonstrates significant improvements on composed query, audio and video retrieval tasks, while achieving on-par performance with state-of-the-art models on others. Furthermore, we curate a new Audio-Centric Multimodal Benchmark (ACM). This new benchmark introduces two critical, previously missing tasks--composed audio retrieval and audio-visual retrieval--to more comprehensively evaluate a model's omni-modal embedding capacity. We believe our benchmark will facilitate the development of universal retrieval systems.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:bf59c667ba317617e9dcca70fd7df7d2CVF · CVPR 2026

Thermal-Det: Language-Guided Cross-Modal Distillation for Open-Vocabulary Thermal Object Detection

Ranasinghe, Yasiru, Schenck, Elim, Yellin, Florence, Hu, Shuowen, Funk, Christopher, Patel, Vishal M.

原文摘要

Existing open-vocabulary detectors focus on RGB images and fail to generalize to thermal imagery, where low texture and emissivity variations challenge RGB-based semantics. We present Thermal-Det, the first large language model (LLM) supervised open-vocabulary detector tailored for thermal images. To enable large-scale training, we construct a synthetic dataset by converting GroundingCap-1M into the thermal domain and filtering captions to remove RGB-specific terms, yielding over one million thermally aligned samples with bounding boxes, grounding texts, and detailed captions. Thermal-Det jointly optimizes detection, captioning, and cross-modal distillation objectives. A frozen RGB teacher provides geometric and semantic pseudo-supervision for paired but unlabeled RGB-thermal data, transferring open-vocabulary knowledge without manual annotation. The model further employs a Thermal-Text Alignment Head for text calibration and a Modality-Fused Cross-Attention module for dual-modality reasoning. Unlike prior domain-adaptation methods, the detector is fully fine-tuned to internalize thermal contrast patterns while preserving language alignment. Experiments on public benchmarks show consistent 2-4% AP gains over existing open-vocabulary detectors, establishing a strong foundation for scalable, language-driven thermal perception.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:bf5eb8c15bbb425c3a7a172966742f0aCVF · CVPR 2026

Learning to See and Act: Task-Aware Virtual View Exploration for Robotic Manipulation

Bai, Yongjie, Wang, Zhouxia, Liu, Yang, Luo, Kaijun, Wen, Yifan, Dai, Mingtong, Chen, Weixing, Chen, Ziliang 等 11 位作者

原文摘要

Recent vision-language-action (VLA) models for multi-task robot manipulation often rely on fixed camera setups and shared visual encoders, which limit their performance under occlusions and during cross-task transfer. To address these challenges, we propose Task-aware Virtual View Exploration (TVVE), a framework that learns to select task-relevant virtual camera viewpoints and dynamically re-render observations from a reconstructed scene representation using the selected viewpoints. To enable efficient view selection, we train an exploration policy in a pseudo-environment. In addition, we introduce a Task-aware Mixture-of-Experts (TaskMoE) visual encoder that routes visual features to task-specialized experts, mitigating interference in multi-task learning. To evaluate robustness under distribution shifts, we construct RLBench-OG, an out-of-distribution benchmark with visual perturbations and camera pose variations. Experiments on RLBench and RLBench-OG demonstrate that TVVE achieves higher success rates than strong baselines, while real-robot experiments further confirm its robustness to visual disturbances and unseen instructions. Code and visualizations are available at: https://hcplab-sysu.github.io/TAVP.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:bf6ef93444e3a71f953e7854f1149111CVF · CVPR 2026

The Devil Is in Gradient Entanglement: Energy-Aware Gradient Coordinator for Robust Generalized Category Discovery

Zheng, Haiyang, Pu, Nan, Cai, Yaqi, Long, Teng, Li, Wenjing, Sebe, Nicu, Zhong, Zhun

原文摘要

Generalized Category Discovery (GCD) leverages labeled data to categorize unlabeled samples from known or unknown classes. Most previous methods jointly optimize supervised and unsupervised objectives and achieve promising results. However, inherent optimization interference still limits their ability to improve further. Through quantitative analysis, we identify a key issue, i.e., gradient entanglement, which 1) distorts supervised gradients and weakens discrimination among known classes, and 2) induces representation-subspace overlap between known and novel classes, reducing the separability of novel categories. To address this issue, we propose the Energy-Aware Gradient Coordinator (EAGC), a plug-and-play gradient-level module that explicitly regulates the optimization process. EAGC comprises two components: Anchor-based Gradient Alignment (AGA) and Energy-aware Elastic Projection (EEP). AGA introduces a reference model to anchor the gradient directions of labeled samples, preserving the discriminative structure of known classes against the interference of unlabeled gradients. EEP softly projects unlabeled gradients onto the complement of the known-class subspace and derives an energy-based coefficient to adaptively scale the projection for each unlabeled sample according to its degree of alignment with the known subspace, thereby reducing subspace overlap without suppressing unlabeled samples that likely belong to known classes. Experiments show that EAGC consistently boosts existing methods and establishes new state-of-the-art results. Code is available at https://haiyangzheng.github.io/EAGC.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:bf7103e5012dba0bcdafd00330950bd8CVF · CVPR 2026

AGFT: Alignment-Guided Fine-Tuning for Zero-Shot Adversarial Robustness of Vision-Language Models

Cui, Yubo, Guan, Xianchao, Xiong, Zijun, Zhang, Zheng

原文摘要

Pre-trained vision-language models (VLMs) exhibit strong zero-shot generalization but remain vulnerable to adversarial perturbations. Existing classification-guided adversarial fine-tuning methods often disrupt pre-trained cross-modal alignment, weakening visual-textual correspondence and degrading zero-shot performance. In this paper, we propose an Alignment-Guided Fine-Tuning (AGFT) framework that enhances zero-shot adversarial robustness while preserving the cross-modal semantic structure. Unlike label-based methods that rely on hard labels and fail to maintain the relative relationships between image and text, AGFT leverages the probabilistic predictions of the original model for text-guided adversarial training, which aligns adversarial visual features with textual embeddings via soft alignment distributions, improving zero-shot adversarial robustness. To address structural discrepancies introduced by fine-tuning, we introduce a distribution consistency calibration mechanism that adjusts the robust model's output to match a temperature-scaled version of the pre-trained model's predictions. Extensive experiments across multiple zero-shot benchmarks demonstrate that AGFT outperforms state-of-the-art methods while significantly improving zero-shot adversarial robustness. Code is available at https://github.com/YuboCui/AGFT.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:bf7e295fc6c2e9c370a63bbad287fa9cCVF · CVPR 2026

Leveraging Verifier-Based Reinforcement Learning in Image Editing

Guo, Hanzhong, Wu, Jie, Liu, Jie, Gao, Yu, Ye, Zilyu, Yuan, Linxiao, Wang, Xionghui, Yu, Yizhou 等 9 位作者

原文摘要

While Reinforcement Learning from Human Feedback (RLHF) has become a pivotal paradigm for text-to-image generation, its application to image editing remains largely unexplored. A key bottleneck is the lack of a robust general reward model for all editing tasks. Existing edit reward models usually give overall scores without detailed checks, ignoring different instruction requirements and causing biased rewards. To address this, we propose the Verifier-Based Reasoning Reward Model (RRM), which breaks instructions into verifiable principles, evaluates the edited images against each principle, and aggregates fine-grained scores to reduce hallucinations and provide more interpretable criteria. To address this, we argue the key is to move from a simple scorer to a reasoning verifier. We introduce Edit-R1, a framework to build a chain-of-thought (CoT) verifier-based reasoning reward model (RRM) and leverage it into the downstream editing task. The Edit-RRM breaks instructions into distinct principles, evaluates the edited image against each, and aggregates these checks to provide an interpretable, fine-grained reward. To build such an RRM, we first apply supervised fine-tuning (SFT) as a "cold-start" to generate CoT reward trajectories. Then, we introduce Group Contrastive Preference Optimization (GCPO), a reinforcement learning algorithm that leverages human pairwise preference data to reinforce our pointwise RRM. After building the RRM, we use GRPO to train editing models with this non-differentiable yet powerful reward model. Extensive experiments demonstrate that our Edit-RRM surpasses powerful VLMs such as Seed-1.5-VL and Seed-1.6-VL as an editing-specific reward model, and we observe a clear scaling trend, with performance consistently improving from 3B to 7B parameters. Moreover, Edit-R1 delivers gains to editing models like FLUX.1-kontext, highlighting its effectiveness in enhancing image editing.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:bf901be07cd24ed5609f36aa0e0c1e77CVF · CVPR 2026

Beyond Soft Label: Dataset Distillation via Orthogonal Gradient Matching

Bo, Deyu, Wang, Xinchao

原文摘要

Condensing the large-scale, high-resolution ImageNet-1K dataset remains a challenge for dataset distillation (DD). Existing methods typically match batch normalization (BN) statistics, i.e., mean and variance, between real and synthetic datasets. Although effective with soft labels, their performance degrades substantially under hard labels. In this paper, we theoretically identify that BN matching mainly aligns the scales of real and synthetic gradients but overlooks their directions. However, experimental evidence demonstrates that gradient direction, rather than scale, is pivotal to model training, clarifying the limitations of prior methods. Building on this insight, we introduce Orthogonal Gradient Matching (OGM), which explicitly aligns the intrinsic direction of gradients, i.e., singular vectors. Specifically, OGM first orthogonalizes real and synthetic gradients by setting all singular values to one, eliminating their scales, and then minimizes the distance between these orthogonal gradients so that their singular vectors coincide. To further reduce computation, OGM employs a least-squares loss whose gradients can be obtained in the forward pass, avoiding back-propagation. Extensive experiments on ImageNet-1K validate the effectiveness of OGM. With only ten images per class (IPC = 10), OGM achieves 47.0% accuracy with soft labels and 16.7% with hard labels, outperforming training-based DD methods and RDED.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:bf9e3c2136ce0f6b162d2edd9ed13ebbCVF · CVPR 2026

See Through the Noise: Improving Domain Generalization in Gaze Estimation

Peng, Yanming, Wang, Shijing, Huang, Yaping, Tian, Yi

原文摘要

Generalizable gaze estimation methods have garnered increasing attention due to their critical importance in real-world applications and have achieved significant progress. However, they often overlook the effect of label noise, arising from the inherent difficulty of acquiring precise gaze annotations, on model generalization performance. In this paper, we are the first to comprehensively investigate the negative effects of label noise on generalization in gaze estimation. Further, we propose a novel solution, called See-Through-Noise (SeeTN) framework, which improves generalization from a novel perspective of mitigating label noise. Specifically, we propose to construct a semantic embedding space via a prototype-based transformation to preserve a consistent topological structure between gaze features and continuous labels. We then measure feature-label affinity consistency to distinguish noisy from clean samples, and introduce a novel affinity regularization in the semantic manifold to transfer gaze-related information from clean to noisy samples. Our proposed SeeTN promotes semantic structure alignment and enforces domain-invariant gaze relationships, thereby enhancing robustness against label noise. Extensive experiments demonstrate that our SeeTN effectively mitigates the adverse impact of source-domain noise, leading to superior cross-domain generalization without compromising the source-domain accuracy, and highlight the importance of explicitly handling noise in generalized gaze estimation.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:bf9e72501610e34d4bc31109655e6d1cCVF · CVPR 2026

UAVLight: A Benchmark for Illumination-Robust 3D Reconstruction in Unmanned Aerial Vehicle (UAV) Scenes

Du, Kang, Liao, Xue, Xia, Junpeng, Guo, Chaozheng, Gu, Yi, Guan, Yirui, Wang, Duotun, Huang, Sheng 等 9 位作者

原文摘要

Illumination inconsistency is a fundamental challenge in multi-view 3D reconstruction. Variations in sunlight direction, cloud cover, and shadows break the constant-lighting assumption underlying both classical multi-view stereo (MVS) and structure from motion (SfM) pipelines and recent neural rendering methods, leading to geometry drift, color inconsistency, and shadow imprinting. This issue is especially critical in UAV-based reconstruction, where long flight durations and outdoor environments make lighting changes unavoidable.However, existing datasets either restrict capture to short time windows, thus lacking meaningful illumination diversity, or span months and seasons, where geometric and semantic changes confound the isolated study of lighting robustness.We introduce UAVLight, a controlled-yet-real benchmark for illumination-robust 3D reconstruction. Each scene is captured along repeatable, geo-referenced flight paths at multiple fixed times of day, producing natural lighting variation under consistent geometry, calibration, and viewpoints. With standardized evaluation protocols across lighting conditions, UAVLight provides a reliable foundation for developing and benchmarking reconstruction methods that are consistent, faithful, and relightable in real outdoor environments.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:bf9f770951ef2d08c4df34d03c7d909cCVF · CVPR 2026

SDTrack: A Baseline for Event-based Tracking via Spiking Neural Networks

Shan, Yimeng, Ren, Zhenbang, Wu, Haodi, Wei, Wenjie, Zhu, Rui-Jie, Wang, Shuai, Zhang, Dehao, Xiao, Yichen 等 14 位作者

原文摘要

Event cameras provide superior temporal resolution, dynamic range, energy efficiency, and pixel bandwidth. Spiking Neural Networks (SNNs) naturally complement event data through discrete spike signals, making them ideal for event-based tracking. However, current approaches combining Artificial Neural Networks (ANNs) and SNNs suffer from suboptimal architectures that compromise energy efficiency and limit tracking performance. To address these limitations, we propose the first Transformer-based Spike-Driven Tracking (SDTrack) pipeline. It incorporates a novel event frame aggregation method called Global Trajectory Prompt (GTP) and a Transformer-based tracker. The GTP method effectively captures global trajectory information and aggregates it with event streams into event frames to enhance spatiotemporal representation. The Transformer-based tracker comprises a fully spike-driven SNN backbone and a simple tracking head. The SDTrack pipeline operates end-to-end without data augmentation or post-processing. Extensive experiments demonstrate that our SDTrack-Tiny pipeline achieves competitive accuracy with only 19.61M parameters and 8.16mJ energy consumption, while our Base version achieves state-of-the-art accuracy across three datasets. Our work establishes a solid foundation for future neuromorphic vision research.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:bfaba5cd48088aaa450b553837ce3089CVF · CVPR 2026

Unified Spatiotemporal Token Compression for Video-LLMs at Ultra-Low Retention

Du, Junhao, Xue, Jialong, Li, Anqi, Dai, Jincheng, Lu, Guo

原文摘要

Video large language models (Video-LLMs) face high computational costs due to large volumes of visual tokens. Existing token compression methods typically adopt a two-stage spatiotemporal compression strategy, relying on stage-specific metrics and an implicit assumption of spatiotemporal separability. Under extremely low retention ratios, however, such approaches often result in unbalanced allocation and loss of visual evidence essential for question answering. We reformulate token compression as a spatiotemporal allocation task within a global token retention pool. We propose a unified selection mechanism that integrates attention weights and semantic similarity to globally select tokens with high contribution and low redundancy. Unselected tokens are merged via clustering and refilled, preserving information integrity. Inside the LLM, we further introduce text-aware merging to perform secondary compression based on query relevance. Without requiring retraining, our method serves as a plug-and-play module compatible with existing Video-LLMs. Experiments show that retaining only about 2% of visual tokens preserves 90.1% of baseline performance across multiple benchmarks, while reducing FLOPs to roughly 2.6%. These benefits generalize across diverse backbones, decreasing end-to-end inference latency and memory consumption. Our unified spatiotemporal token compression strategy establishes the state-of-the-art in video understanding under ultra-low token retention.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:bfd8a67cd51bc971effe4dfa9c3018e8CVF · CVPR 2026

Adapting In-context Generation for Enhanced Composed Image Retrieval

Li, Haiwen, Chen, Zining, Liu, Delong, Hou, Zhaohui, Zhao, Zhicheng, Su, Fei

原文摘要

As a challenge vision-language task, Composed Image Retrieval (CIR) aims to integrate information from a bi-modal query (image + text) to retrieve target images. While supervised CIR has achieved notable success in domain-specific scenarios, its reliance on manually annotated triplets restricts its scalability and application. Zero-shot CIR alleviates this by leveraging unlabeled data or automatically collected triplets, yet it often suffers from an intractable domain gap. To this end, we shift the focus to developing robust CIR models under limited labeled data and propose Domain-Adaptive In-context Generation (DAIG), which adapts the in-context capability of a pretrained Text-to-Image (T2I) model to the target domain and the CIR task using few-shot samples and then transforms the LLM-generated textual triplets into unbiased CIR triplets as additional training data. After that, we present a two-stage framework applicable to any supervised CIR approach. The first stage, Distributionally Robust Synthetic Pretraining (DRSP), perturbs visual features to expand the distribution of synthetic data and improve training robustness on it. The second stage, Fine-grained Real-world Adaptation (FRA), fine-tunes on manually annotated triplets by imposing an angular margin on matching pairs to facilitate fine-grained learning. Experiments on two benchmarks validate the effectiveness of our method, i.e., under both few-shot and fully supervised CIR settings, DAIG yields substantial performance gains over CLIP4CIR, BLIP4CIR, and SPRC. The code is available at \href https://github.com/JThuge/DAIG \color blue!37https://github.com/JThuge/DAIG .

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:c0026393318c579eacc88c8277869c2cCVF · CVPR 2026

Reviving ConvNeXt for Efficient Convolutional Diffusion Models

Kwon, Taesung, Bianchi, Lorenzo, Wittke, Lennart, Watine, Felix, Carrara, Fabio, Ye, Jong Chul, Weber, Romann, Azevedo, Vinicius

原文摘要

Recent diffusion models increasingly favor Transformer backbones, motivated by the remarkable scalability of fully attentional architectures. Yet the locality bias, parameter efficiency, and hardware friendliness--the attributes that established ConvNets as the efficient vision backbone--have seen limited exploration in modern generative modeling. Here we introduce the fully convolutional diffusion model (FCDM), a model having a backbone similar to ConvNeXt, but designed for conditional diffusion modeling. We find that using only 50% of the FLOPs of DiT-XL/2, FCDM-XL achieves competitive performance with 7x and 7.5x fewer training steps at 256x256 and 512x512 resolutions, respectively. Remarkably, FCDM-XL can be trained on a 4-GPU system, highlighting the exceptional training efficiency of our architecture. Our results demonstrate that modern convolutional designs provide a competitive and highly efficient alternative for scaling diffusion models, reviving ConvNeXt as a simple yet powerful building block for efficient generative modeling.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:c006753815df293f63d5434dd466b1caCVF · CVPR 2026

AKCMamba-YOLO: Selective State Space Models For Real-Time Object Detection

Chen, Long, Wang, Hui, Xu, Man, Li, Zexuan, Fan, Zizhu

原文摘要

The YOLO (You Only Look Once) series has been a cornerstone in real-time object detection, renowned for its efficient convolutional design and rapid inference. However, its reliance on convolutional operations inherently limits its ability to capture long-range dependencies and rich contextual information, leading to suboptimal performance in complex scenes. Recently, SSM (State Space Models) have emerged as an efficient alternative to attention mechanisms, offering global representation with linear time complexity. In this paper, we propose AKCMamba-YOLO, a novel object detector that incorporates SSM into the YOLO architecture. We introduce 3CAKCMamba and 4CAKCMamba modules to a novel object detection framework, enabling enhanced channel interaction and cross-layer semantic fusion. This design improves multi-scale feature modeling while maintaining computational efficiency. To support safety-critical applications, we provide railway pedestrian Detection datasets with 2,975 annotated images under complex scenarios. Experiments on COCO2017, power tower foreign object detection datasets, and our custom dataset show that AKCMamba-YOLO achieves superior accuracy and speed compared to state-of-the-art baselines, making it well-suited for real-time and resource-constrained environments.

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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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Semi-supervised Echocardiography Video Segmentation via Anchor Semantic Awareness and Continuous Pseudo-label Reforging

Fang, Yunpeng, Sun, Yimu, Guo, Jingxing, Wu, Huisi, Qin, Jing

原文摘要

Automatic and accurate echocardiography video segmentation is essential for efficient and repeatable measurements of key clinical functional indicators for the diagnosis of cardiovascular diseases. However, it is an extremely challenging task to obtain high-quality segmentation results throughout the cardiac cycle owing to (1) the inherent speckle noise in echocardiography videos, (2) the complex dynamic motions of cardiac structures, and (3) the scarcity of annotated data. To comprehensively address these challenges, we propose a novel semi-supervised model, EchoForge, which can achieve accurate and real-time echocardiography video segmentation with very limited annotations. EchoForge introduces an Anchor Semantic Awareness (ASA) module that refines ambiguous regions using learnable anchors and propagates structural prototypes across frames to enhance boundary delineation and temporal consistency. Building upon ASA, a Continuous Pseudo-label Reforging (CPR) module progressively integrates and refines pseudo-labels via channel-wise attention, providing robust supervision. Extensive experiments on the CAMUS and EchoNet-Dynamic benchmarks demonstrate that EchoForge outperforms state-of-the-art (SOTA) methods in accuracy while maintaining real-time efficiency. The code is available at https://github.com/YunPeng-Fang/EchoForge.

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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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MaskDexGrasp: Generative Masked Modeling for Part-Aware Dexterous Grasp Synthesis

Zuo, Binghui, Zhou, Lin, Xu, Haoxuan, Yan, Jianan, Yu, Zhipeng, Liu, Zekai, Wang, Yangang

原文摘要

Dexterous grasp generation is a predominant task that enables robots to perform human-level manipulation. However, a dexterous hand always maintains high-dimensional DoF and actuation space, making existing approaches that rely on holistic latent representations difficult to produce high-quality and semantically aligned grasps. In this paper, we propose MaskDexGrasp to address these challenges. We first present a part-aware grasp tokenizer that decomposes dexterous grasps into discrete tokens, facilitating compositional modeling of anatomical dependencies. Building upon this representation, a bidirectional masked grasp transformer is then developed to predict grasp tokens conditioned on object geometry and task description, ensuring coherent grasp generation while allowing fine-grained part-level editing. To facilitate evaluation, we construct a dexterous grasp dataset that comprises 65K grasping instances and 260K richly annotated descriptions covering 11 tasks. Comprehensive experiments demonstrate that our method achieves the state-of-the-art performance. Project page is available at https://binghui-z.github.io/MaskDexGrasp/.

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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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Bridging Domain Expertise and Generalization for Performance Estimation

Li, Shuxuan, Zhao, Zhilin, Kong, Quyu, Zheng, Wei-Shi

原文摘要

Performance estimation under distribution shift aims to predict how a model behaves on an unlabeled test set whose distribution differs from the training data, a scenario that requires reliable indicators that can faithfully reflect model behavior without ground-truth labels. Existing approaches rely solely on the outputs of the given model whose biases are amplified once the distribution shifts, weakening the correlation with the true performance. Motivated by this limitation, we propose Fused Reference Alignment Prediction (FRAP), which leverages the complementary strengths of an external foundation model and the base model to construct a more reliable surrogate of the ground-truth labels. FRAP aligns the prediction distribution of the foundation model with that of the base model by applying temperature-scaled calibration that minimizes their divergence. The aligned predictions are fused through confidence-based weighting into a refined reference distribution that integrates robustness from the foundation model and domain-specific expertise from the base model, and performance estimation is obtained by measuring how closely the base model predictions agree with this reference. Extensive experiments across diverse datasets and architectures show that FRAP provides consistent and substantial improvements over representative performance-estimation methods under distribution shift.

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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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Affordance-First Decomposition for Continual Learning in Video-Language Understanding

xu, Mengzhu, Liu, Hanzhi, Peng, Ningkang, Chen, Qianyu, Xiao, Canran

原文摘要

Continual learning for video--language understanding is increasingly important as models face non-stationary data, domains, and query styles, yet prevailing solutions blur what should stay stable versus what should adapt, rely on static routing/capacity, or require replaying past videos. We aim to explicitly specify where stability lives and where plasticity should be focused under realistic memory and privacy constraints. We introduce Affordance-First Decomposition (AFD): videos are mapped to slowly varying affordance tokens that form a shared, time-aligned substrate, while a lightweight, query-routed, conflict-aware scheduler concentrates adaptation and grows capacity only when needed. The substrate is stabilized via weak alignment and teacher consistency, and training uses question-only replay. AFD achieves state-of-the-art across protocols: 51.6% average accuracy with -1.8% forgetting on domain-incremental VideoQA, ViLCo R@1@0.5 of 29.6% (MQ) and 20.7% (NLQ) with 18.4% stAP@0.25 (VQ), and 39.5% accuracy with -1.6% forgetting on time-incremental iVQA. Overall, AFD offers an explicit, interpretable split between a stable interaction-centered substrate and targeted adaptation.

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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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SPDMark: Selective Parameter Displacement for Robust Video Watermarking

Fares, Samar, Tastan, Nurbek, Nandakumar, Karthik

原文摘要

The advent of high-quality video generation models has amplified the need for robust watermarking schemes that can be used to reliably detect and track the provenance of generated videos. Existing video watermarking methods based on both post-hoc and in-generation approaches fail to simultaneously achieve imperceptibility, robustness, and computational efficiency. This work introduces a novel framework for in-generation video watermarking called \texttt SPDMark (pronounced `SpeedMark') based on selective parameter displacement of a video diffusion model. Watermarks are embedded into the generated videos by modifying a subset of parameters in the generative model. To make the problem tractable, the displacement is modeled as an additive composition of layer-wise basis shifts, where the final composition is indexed by the watermarking key. For parameter efficiency, this work specifically leverages low-rank adaptation (LoRA) to implement the basis shifts. During the training phase, the basis shifts and the watermark extractor are jointly learned by minimizing a combination of message recovery, perceptual similarity, and temporal consistency losses. To detect and localize temporal modifications in the watermarked videos, we use a cryptographic hashing function to derive frame-specific watermark messages from the given base watermarking key. During watermark extraction, maximum bipartite matching is applied to recover the correct frame order, even from temporally tampered videos. Evaluations on both text-to-video and image-to-video generation models demonstrate the ability of \texttt SPDMark to generate imperceptible watermarks that can be recovered with high accuracy and also establish its robustness against a variety of common video modifications.

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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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Identity-Preserving Image-to-Video Generation via Reward-Guided Optimization

Shen, Liao, Jiang, Wentao, Zhu, Yiran, Li, Jiahe, Ge, Tiezheng, Cao, Zhiguo, Zheng, Bo

原文摘要

Recent advances in image-to-video (I2V) generation have achieved remarkable progress in synthesizing high-quality, temporally coherent videos from static images. Among all the applications of I2V, human-centric video generation includes a large portion. However, existing I2V models encounter difficulties in maintaining identity consistency between the input human image and the generated video, especially when the person in the video exhibits significant expression changes and movements. This issue becomes critical when the human face occupies merely a small fraction of the image. Since humans are highly sensitive to identity variations, this poses a critical yet under-explored challenge in I2V generation. In this paper, we propose Identity-Preserving Reward-guided Optimization (IPRO), a novel video diffusion framework based on reinforcement learning to enhance identity preservation. Instead of introducing auxiliary modules or altering model architectures, our approach introduces a direct and effective tuning algorithm that optimizes diffusion models using a face identity scorer. To improve performance and accelerate convergence, our method backpropagates the reward signal through the last steps of the sampling chain, enabling richer gradient feedback. We also propose a novel facial scoring mechanism that treats faces in ground-truth videos as facial feature pools, providing multi-angle facial information to enhance generalization. A KL-divergence regularization is further incorporated to stabilize training and prevent overfitting to the reward signal. Extensive experiments on Wan 2.2 I2V model and our in-house I2V model demonstrate the effectiveness of our method. Our project is available at https://dof-gaussian.github.io/.

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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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Geometry-Aware Cross-Modal Graph Alignment for Referring Segmentation in 3D Gaussian Splatting

Tao, Yuwen, Zhou, Kanglei, Li, Chang, Wang, Liyuan

原文摘要

Referring 3D segmentation seeks to localize and segment target objects in a 3D scene given a natural-language query, requiring joint reasoning over geometric structures and linguistic cues. Although recent progress using 3D Gaussian Splatting (3DGS) has improved rendering quality, existing methods still struggle to spatially ground textual references due to two fundamental limitations: (1) language encoders provide no explicit positional priors, weakening geometric relation modeling; and (2) cross-modal attention is self-reinforcing, causing spatial errors to propagate through the Gaussian field once misalignment occurs. To address this, we propose GeoCGA, a geometry-aware cross-modal graph alignment framework that bridges linguistic semantics with the 3DGS representation. GeoCGA introduces position-aware prompt expansion to build a semantic-spatial graph capturing relational structure in text, and constructs a Gaussian-based geometric graph encoding 3D topology. A cross-modal alignment module enforces geometric consistency between the two graphs, enabling stable and spatially grounded correspondence across views. GeoCGA consistently outperforms prior state-of-the-art methods, yielding relative mIoU improvements of 20.8% on Ref-LERF, 5.7% on LERF-OVS, and 1.0% on 3D-OVS.

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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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TopoCL: Topological Contrastive Learning for Medical Imaging

Meng, Guangyu, Gu, Pengfei, Liang, Peixian, Lalor, John P., Chambers, Erin Wolf, Chen, Danny Z.

原文摘要

Contrastive learning (CL) has become a powerful approach for learning representations from unlabeled images. However, existing CL methods focus predominantly on visual appearance features while neglecting topological characteristics (e.g., connectivity patterns, boundary configurations, cavity formations) that provide valuable cues for medical image analysis. To address this limitation, we propose a new topological CL framework (TopoCL) that explicitly exploits topological structures during contrastive learning for medical imaging. Specifically, we first introduce topology-aware augmentations that control topological perturbations using a relative bottleneck distance between persistence diagrams, preserving medically relevant topological properties while enabling controlled structural variations. We then design a Hierarchical Topology Encoder that captures topological features through self-attention and cross-attention mechanisms. Finally, we develop an adaptive mixture-of-experts (MoE) module to dynamically integrate visual and topological representations. TopoCL can be seamlessly integrated with existing CL methods. We evaluate TopoCL on five representative CL methods (SimCLR, MoCo-v3, BYOL, DINO, and Barlow Twins) and five diverse medical image classification datasets. The experimental results show that TopoCL achieves consistent improvements: an average gain of 3.26% in linear probe classification accuracy with strong statistical significance, verifying its effectiveness.

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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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AV-Reasoner: Improving and Benchmarking Clue-Grounded Audio-Visual Counting for MLLMs

Lu, Lidong, Chen, Guo, Wei, Zhu, Li, Zhiqi, Liu, Yicheng, Lu, Tong

原文摘要

Despite progress in video understanding, current MLLMs struggle with counting tasks. Existing benchmarks are limited by short videos, close-set queries, lack of clue annotations, and weak multimodal coverage. In this paper, we introduce CG-AV-Counting, a manually-annotated clue-grounded counting benchmark with 1,027 multimodal questions and 5,845 annotated clues over 497 long videos. It supports both black-box and white-box evaluation, serving as a comprehensive testbed for both end-to-end and reasoning-based counting. To explore ways to improve model's counting capability, we propose AV-Reasoner, a model trained with GRPO and curriculum learning to generalize counting ability from related tasks. AV-Reasoner achieves SOTA results across multiple benchmarks, demonstrating the effectiveness of reinforcement learning. However, experiments reveal that on out-of-domain benchmarks, reasoning in the language space offers limited performance gains, suggesting the need for more robust cross-domain reasoning mechanisms.

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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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OMG-Bench: A New Challenging Benchmark for Skeleton-based Online Micro Hand Gesture Recognition

Chang, Haochen, Ren, Pengfei, Zhang, Buyuan, Li, Da, Han, Tianhao, Zhang, Haoyang, Xie, Liang, Chen, Hongbo 等 9 位作者

原文摘要

Online micro gesture recognition from hand skeletons is critical for VR/AR interaction but faces challenges due to limited public datasets and task-specific algorithms. Micro gestures involve subtle motion patterns, which make constructing datasets with precise skeletons and frame-level annotations difficult. To this end, we develop a multi-view self-supervised pipeline to automatically generate skeleton data, complemented by heuristic rules and expert refinement for semi-automatic annotation. Based on this pipeline, we introduce OMG-Bench, the first large-scale public benchmark for skeleton-based online micro gesture recognition. It features 40 fine-grained gesture classes with 13,948 instances across 1,272 sequences, characterized by subtle motions, rapid dynamics, and continuous execution. To tackle these challenges, we propose Hierarchical Memory-Augmented Transformer (HMATr), an end-to-end framework that unifies gesture detection and classification by leveraging hierarchical memory banks which store frame-level details and window-level semantics to preserve historical context. In addition, it employs learnable position-aware queries initialized from the memory to implicitly encode gesture positions and semantics. Experiments show that HMATr outperforms state-of-the-art methods by 7.6% in detection rate, establishing a strong baseline for online micro gesture recognition.

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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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Bayesian Decomposition and Semantic Completion for Few-shot Semantic Segmentation

Shi, Guangchen, Wu, Yirui, Zhu, Wei, Wang, Tao, Zhang, Hao, Li, Bo, Lu, Tong

原文摘要

Few-shot Semantic Segmentation (FSS) aims to segment objects of novel categories given only a handful of labeled examples. However, existing methods often rely on complex category-specific modeling, resulting in high computational cost and limited generalization under low-data regimes. To address these challenges, we propose a Bayesian Probabilistic Network (BPNet) that reformulates FSS as a composition of three interpretable components: a prior, a likelihood, and a class-consistency term. Specifically, an efficient Segment Anything Model (SAM) is employed to generate fragmented prior regions for the query image, while both the likelihood and the consistency terms are estimated by a lightweight Class-Agnostic Localization Model (CALM). CALM simultaneously predicts the class consistency between support-query pairs through a binary classification head and estimates the likelihood by localizing the target region in the support image. By evaluating SAM-generated regions in parallel, CALM can efficiently identify the core region, thereby transforming the segmentation problem into a simple binary classification task. Furthermore, to mitigate the semantic incompleteness of SAM proposals, we introduce an attention-based Semantic Completion Module (SCM), which leverages local and global context cues to integrate fragmented regions into semantically complete masks. Extensive experiments demonstrate that BPNet achieves state-of-the-art performance while maintaining high efficiency.

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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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SLARM: Streaming and Language-Aligned Reconstruction Model for Dynamic Scenes

Qiu, Zhicheng, Meng, Jiarui, Luo, Tong-an, Huang, Yican, Feng, Xuan, Li, Xuanfu, Xu, Zhan

原文摘要

We propose SLARM, a feed-forward model that unifies dynamic scene reconstruction, semantic understanding, and real-time streaming inference. SLARM captures complex, non-uniform motion through higher-order motion modeling, trained solely on differentiable renderings without any flow supervision. Besides, SLARM distills semantic features from LSeg to obtain language-aligned representations. This design enables semantic querying via natural language, and the tight coupling between semantics and geometry further enhances the accuracy and robustness of dynamic reconstruction. Moreover, SLARM processes image sequences using window-based causal attention, achieving stable, low-latency streaming inference without accumulating memory cost. Within this unified framework, SLARM achieves state-of-the-art results in dynamic estimation, rendering quality, and scene parsing, improving motion accuracy by 21%, reconstruction PSNR by 1.6 dB, and segmentation mIoU by 20% over existing methods.

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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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Rethinking Diffusion Model-Based Video Super-Resolution: Leveraging Dense Guidance from Aligned Features

Xu, Jingyi, Zheng, Meisong, Chen, Ying, Qiao, Minglang, Deng, Xin, Xu, Mai

原文摘要

Diffusion model (DM) based Video Super-Resolution (VSR) approaches achieve impressive perceptual quality. Diffusion model (DM) based Video Super-Resolution (VSR) approaches achieve impressive perceptual quality. However, existing DM-based VSR methods over-prioritize perceptual synthesis while neglecting fidelity gains from accurate alignment and sufficient compensation. In this paper, within the DM-based VSR pipeline, we revisit the role of alignment and compensation between adjacent video frames and reveal two crucial observations: (a) the feature domain is better suited than the pixel domain for information compensation due to its stronger spatial and temporal correlations, and (b) warping at an upscaled resolution better preserves high-frequency information, but this benefit is not necessarily monotonic. Therefore, we propose a novel Densely Guided diffusion model with Aligned Features for Video Super-Resolution (DGAF-VSR), with an Optical Guided Warping Module (OGWM) to maintain high-frequency details in the aligned features and a Feature-wise Temporal Condition Module (FTCM) to deliver dense guidance in the feature domain. Extensive experiments on synthetic and real-world datasets demonstrate that DGAF-VSR surpasses state-of-the-art methods in key aspects of VSR, including perceptual quality (35.82% DISTS reduction), fidelity (0.20 dB PSNR gain), and temporal consistency (30.37% tLPIPS reduction).

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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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